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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/12105

Title: Probabilistic modeling for an integrated temporary acquired immunity with norovirus epidemiological data
Authors: Owusu-Ansah, Emmanuel de-Graft Johnson
Barnes, Benedict
Abaidoo, Robert
Tine, Hald
Dalsgaard, Anders
Permin, Anders
Schou, Torben Wilde
Keywords: Quantitative risk assessment
Probabilistic modeling
Immunity integrated modeling
Issue Date: Apr-2019
Publisher: Infectious Disease Modelling
Citation: Infectious Disease Modelling,
Abstract: Integration of acquired immunity into microbial risk assessment for illness incidence is of no doubt essential for the study of susceptibility to illness. In this study, a probabilistic model was set up as dose response for infection and a mathematical derivation was carried out by integrating immunity to obtain probability of illness models. Temporary acquire immunity from epidemiology studies which includes six different Norovirus transmission scenarios such as symptomatic individuals infectious, pre- and post-symptomatic infectiousness (low and high), innate genetic resistance, genogroup 2 type 4 and those with no immune boosting by asymptomatic infection were evaluated. Simulated results on illness inflation factor as a function of dose and exposure indicated that high frequency exposures had immense immunity build up even at high dose levels; hence minimized the probability of illness. Using Norovirus transmission dynamics data, results showed, and immunity included models had a reduction of 2e6 logs of magnitude difference in disease burden for both population and individual probable illness incidence. Additionally, the magnitude order of illness for each dose response remained largely the same for all transmission scenarios; symptomatic infectiousness and no immune boosting after asymptomatic infectiousness also remained the same throughout. With integration of epidemiological data on acquired immunity into the risk assessment, more realistic results were achieved signifying an overestimation of probable risk of illness when epidemiological immunity data are not included. This finding supported the call for rigorous
Description: This article is published in Infectious Disease Modelling and also available at https://doi.org/10.1016/j.idm.2019.04.005
URI: doi.org/10.1016/j.idm.2019.04.005
http://hdl.handle.net/123456789/12105
Appears in Collections:College of Science

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